Foundations of Artificial Intelligence

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1 Foundations of Artificial Intelligence 7. Propositional Logic Rational Thinking, Logic, Resolution Wolfram Burgard, Bernhard Nebel and Martin Riedmiller Albert-Ludwigs-Universität Freiburg

2 Contents 1 Agents that Think Rationally 2 The Wumpus World 3 Propositional Logic: Syntax and Semantics 4 Logical Entailment 5 Logical Derivation (Resolution) (University of Freiburg) Foundations of AI 2 / 42

3 Agents that Think Rationally Until now, the focus has been on agents that act rationally. Often, however, rational action requires rational (logical) thought on the agent s part. To that purpose, portions of the world must be represented in a knowledge base, or KB. A KB is composed of sentences in a language with a truth theory (logic), i.e., we (being external) can interpret sentences as statements about the world. (semantics) Through their form, the sentences themselves have a causal influence on the agent s behavior in a way that is correlated with the contents of the sentences. (syntax) Interaction with the KB through Ask and Tell (simplified): Ask(KB,α) = yes exactly when α follows from the KB Tell(KB,α) = KB so that α follows from KB Forget(KB,α) = KB non-monotonic (will not be discussed) (University of Freiburg) Foundations of AI 3 / 42

4 3 Levels In the context of knowledge representation, we can distinguish three levels [Newell 1990]: Knowledge level: Most abstract level. Concerns the total knowledge contained in the KB. For example, the automated DB information system knows that a trip from Freiburg to Basel SBB with an ICE costs e. Logical level: Encoding of knowledge in a formal language. Price(Freiburg, Basel, 24.70) Implementation level: The internal representation of the sentences, for example: As a string Price(Freiburg, Basel, 24.70) As a value in a matrix When Ask and Tell are working correctly, it is possible to remain on the knowledge level. Advantage: very comfortable user interface. The user has his/her own mental model of the world (statements about the world) and communicates it to the agent (Tell). (University of Freiburg) Foundations of AI 4 / 42

5 7 A Knowledge-Based Agent LOGICAL AGENTS A knowledge-based agent uses its knowledge base to represent its background knowledge store its observations store its executed actions... derive actions function KB-AGENT(percept ) returns an action persistent: KB, a knowledge base t, a counter, initially 0, indicating time TELL(KB, MAKE-PERCEPT-SENTENCE( percept, t)) action ASK(KB, MAKE-ACTION-QUERY(t)) TELL(KB, MAKE-ACTION-SENTENCE(action, t)) t t + 1 return action Figure 7.1 A generic knowledge-based agent. Given a percept, the agen (University of Freiburg) Foundations of AI 5 / 42

6 The Wumpus World (1) A 4 4 grid In the square containing the wumpus and in the directly adjacent squares, the agent perceives a stench. In the squares adjacent to a pit, the agent perceives a breeze. In the square where the gold is, the agent perceives a glitter. When the agent walks into a wall, it perceives a bump. When the wumpus is killed, its scream is heard everywhere. Percepts are represented as a 5-tuple, e.g., [Stench, Breeze, Glitter, None, None] means that it stinks, there is a breeze and a glitter, but no bump and no scream. The agent cannot perceive its own location, cannot look in adjacent square. (University of Freiburg) Foundations of AI 6 / 42

7 The Wumpus World (2) Actions: Go forward, turn right by 90, turn left by 90, pick up an object in the same square (grab), shoot (there is only one arrow), leave the cave (only works in square [1,1]). The agent dies if it falls down a pit or meets a live wumpus. Initial situation: The agent is in square [1,1] facing east. Somewhere exists a wumpus, a pile of gold and 3 pits. Goal: Find the gold and leave the cave. (University of Freiburg) Foundations of AI 7 / 42

8 The Wumpus World (3): A Sample Configuration 4 Stench Breez e PIT Breez e 3 Stench PIT Breez e Gold 2 Stench Breez e 1 Breez e PIT Breez e START (University of Freiburg) Foundations of AI 8 / 42

9 The Wumpus World (4) [1,2] and [2,1] are safe: 1,4 2,4 3,4 4,4 1,3 2,3 3,3 4,3 1,2 2,2 3,2 4,2 OK 1,1 2,1 3,1 4,1 A OK OK (a) A = Agent B = Breeze G = Glitter, Gold OK = Safe square P = Pit S = Stench V = Visited W = Wumpus 1,4 2,4 3,4 4,4 1,3 2,3 3,3 4,3 1,2 2,2 3,2 4,2 P? OK 1,1 2,1 A 3,1 P? 4,1 V B OK OK (b) (University of Freiburg) Foundations of AI 9 / 42

10 The Wumpus World (5) The wumpus is in [1,3]! 1,4 2,4 3,4 4,4 1,3 W! 2,3 3,3 4,3 1,2 A 2,2 3,2 4,2 S OK OK 1,1 2,1 B 3,1 P! 4,1 V V OK OK (a) A = Agent B = Breeze G = Glitter, Gold OK = Safe square P = Pit S = Stench V = Visited W = Wumpus 1,4 2,4 3,4 4,4 P? 1,3 W! 2,3 A 3,3 P? 4,3 S G B 1,2 S 2,2 3,2 4,2 V V OK OK 1,1 2,1 B 3,1 P! 4,1 V V OK OK (b) (University of Freiburg) Foundations of AI 10 / 42

11 Declarative Languages Before a system that is capable of learning, thinking, planning, explaining,... can be built, one must find a way to express knowledge. We need a precise, declarative language. Declarative: System believes P if and only if (iff) it considers P to be true (one cannot believe P without an idea of what it means for the world to fulfill P). Precise: We must know, - which symbols represent sentences, - what it means for a sentence to be true, and - when a sentence follows from other sentences. One possibility: Propositional Logic (University of Freiburg) Foundations of AI 11 / 42

12 Basics of Propositional Logic (1) Propositions: The building blocks of propositional logic are indivisible, atomic statements (atomic propositions), e.g., The block is red, expressed e.g. by symbol B red The wumpus is in [1,3], expressed e.g. by symbol W 1,3 and the logical connectives and, or, and not, which we can use to build formulae. (University of Freiburg) Foundations of AI 12 / 42

13 Basics of Propositional Logic (2) We are interested in knowing the following: When is a proposition true? When does a proposition follow from a knowledge base (KB)? Symbolically: KB = ϕ Can we (syntactically) define the concept of derivation, Symbolically: KB ϕ such that it is equivalent to the concept of logical implication? Meaning and implementation of Ask (University of Freiburg) Foundations of AI 13 / 42

14 Syntax of Propositional Logic Countable alphabet Σ of atomic propositions: P, Q, R,... Logical formulae: P Σ ϕ ϕ ψ ϕ ψ ϕ ψ ϕ ψ atomic formula falseness truth negation conjunction disjunction implication equivalence Operator precedence: > > > >. (use brackets when necessary) Atom: atomic formula Literal: (possibly negated) atomic formula Clause: disjunction of literals (University of Freiburg) Foundations of AI 14 / 42

15 Semantics: Intuition Atomic propositions can be true (T ) or false (F ). The truth of a formula follows from the truth of its atomic propositions (truth assignment or interpretation) and the connectives. Example: (P Q) R If P and Q are false and R is true, the formula is false If P and R are true, the formula is true regardless of what Q is. (University of Freiburg) Foundations of AI 15 / 42

16 Semantics: Formally A truth assignment of the atoms in Σ, or an interpretation I over Σ, is a function I : Σ {T, F } Interpretation I satisfies a formula ϕ ( I = ϕ ): I = I = I = P iff P I = T I = ϕ iff I = ϕ I = ϕ ψ iff I = ϕ and I = ψ I = ϕ ψ iff I = ϕ or I = ψ I = ϕ ψ iff if I = ϕ, then I = ψ I = ϕ ψ iff if I = ϕ if and only if I = ψ I satisfies ϕ (I = ϕ) or ϕ is true under I, when I(ϕ) = T. I can be seen as a possible world (University of Freiburg) Foundations of AI 16 / 42

17 Example P T Q T I : R F S F Question: I = ϕ? ϕ = ((P Q) (R S)) ( (P Q) (R S)) (University of Freiburg) Foundations of AI 17 / 42

18 Terminology An interpretation I is called a model of ϕ if I = ϕ. An interpretation is a model of a set of formulae if it fulfils all formulae of the set. A formula ϕ is satisfiable if there exists I that satisfies ϕ, unsatisfiable if ϕ is not satisfiable, falsifiable if there exists I that doesn t satisfy ϕ, and valid (a tautology) if I = ϕ holds for all I. Two formulae are logically equivalent (ϕ ψ) if I = ϕ iff I = ψ holds for all I. (University of Freiburg) Foundations of AI 18 / 42

19 The Truth Table Method How can we decide if a formula is satisfiable, valid, etc.? Generate a truth table Example: Is ϕ = ((P H) H) P valid? P H P H (P H) H (P H) H P F F F F T F T T F T T F T T T T T T F T Since the formula is true for all possible combinations of truth values (satisfied under all interpretations), ϕ is valid. Satisfiability, falsifiability, unsatisfiability likewise. (University of Freiburg) Foundations of AI 19 / 42

20 Making Logical Implications Goal: Find an algorithmic way to derive new knowledge out of knowledge base 1 Transform KB into a standardized representation 2 define rules that syntactically modify formulae while keeping semantic correctness (University of Freiburg) Foundations of AI 20 / 42

21 Wumpus World in Propositional Logic Symbols: B 1,1, B 1,2,...B 2,1,...S 1,1,...P 1,1,...W 1,1,... Meaning: B = Breeze, B i,j = there is a breeze in (i, j) etc. Rules: R1: P 1,1 R2: B 1,1 (P 1,2 P 2,1 ) R3: B 2,1 (P 1,2 P 2,2 P 3,1 ) R4: B 1,1 (percept) R5: B 2,1 (percept)... (University of Freiburg) Foundations of AI 21 / 42

22 Normal Forms A formula is in conjunctive normal form (CNF) if it consists of a conjunction of disjunctions of literals l i,j, i.e., if it has the following form: ( n mi ) i=1 j=1 l i,j A formula is in disjunctive normal form (DNF) if it consists of a disjunction of conjunctions of literals: ( n mi ) i=1 j=1 l i,j For every formula, there exists at least one equivalent formula in CNF and one in DNF. A formula in DNF is satisfiable iff one disjunct is satisfiable. A formula in CNF is valid iff every conjunct is valid. (University of Freiburg) Foundations of AI 22 / 42

23 Producing CNF 1. Eliminate and : α β ( α β) etc. 2. Move inwards: (α β) ( α β) etc. 3. Distribute over : ((α β) γ) (α γ) (β γ) 4. Simplify: α α α etc. The result is a conjunction of disjunctions of literals An analogous process converts any formula to an equivalent formula in DNF. During conversion, formulae can expand exponentially. Note: Conversion to CNF formula can be done polynomially if only satisfiability should be preserved (University of Freiburg) Foundations of AI 23 / 42

24 Logical Implication: Intuition A set of formulae (a KB) usually provides an incomplete description of the world, i.e., leaves the truth values of a proposition open. Example: KB = {(P Q) (R P ) S} is definitive with respect to S, but leaves P, Q, R open (although they cannot take on arbitrary values). Models of the KB: P Q R S F T F T F T T T T F T T T T T T In all models of the KB, Q R is true, i.e., Q R follows logically from KB. (University of Freiburg) Foundations of AI 24 / 42

25 Logical Implication: Formal The formula ϕ follows logically from the KB if ϕ is true in all models of the KB (symbolically KB = ϕ): KB = ϕ iff I = ϕ for all models I of KB Note: The = symbol is a meta-symbol Question: Can we determine KB = ϕ without considering all interpretations (the truth table method)? Some properties of logical implication relationships: Deduction theorem: KB {ϕ} = ψ iff KB = ϕ ψ Contraposition theorem: KB {ϕ} = ψ iff KB {ψ} = ϕ Contradiction theorem: KB {ϕ} is unsatisfiable iff KB = ϕ (University of Freiburg) Foundations of AI 25 / 42

26 Proof of the Deduction Theorem Assumption: KB {ϕ} = ψ, i.e., every model of KB {ϕ} is also a model of ψ. Let I be any model of KB. If I is also a model of ϕ, then it follows that I is also a model of ψ. This means that I is also a model of ϕ ψ, i.e., KB = ϕ ψ. Assumption: KB = ϕ ψ. Let I be any model of KB that is also a model of ϕ, i.e., I = KB {ϕ}. From the assumption, I is also a model of ϕ ψ and thereby also of ψ, i.e., KB {ϕ} = ψ. (University of Freiburg) Foundations of AI 26 / 42

27 Proof of the Contraposition Theorem KB {ϕ} = ψ iff KB = ϕ ψ (1) iff KB = ( ϕ ψ) iff KB = ( ψ ϕ) iff KB = ψ ϕ iff KB {ψ} = ϕ (2) Note: (1) and (2) are applications of the deduction theorem. (University of Freiburg) Foundations of AI 27 / 42

28 Inference Rules, Calculi, and Proofs We can often derive new formulae from formulae in the KB. These new formulae should follow logically from the syntactical structure of the KB formulae. Example: If the KB is {..., (ϕ ψ),..., ϕ,...} then ψ is a logical consequence of KB Inference rules, e.g., ϕ, ϕ ψ ψ Calculus: Set of inference rules (potentially including so-called logical axioms) Proof step: Application of an inference rule on a set of formulae. Proof: Sequence of proof steps where every newly-derived formula is added, and in the last step, the goal formula is produced. (University of Freiburg) Foundations of AI 28 / 42

29 Soundness and Completeness In the case where in the calculus C there is a proof for a formula ϕ, we write KB C ϕ (optionally without subscript C). A calculus C is sound (or correct) if all formulae that are derivable from a KB actually follow logically. KB C ϕ implies KB = ϕ This normally follows from the soundness of the inference rules and the logical axioms. A calculus is complete if every formula that follows logically from the KB is also derivable with C from the KB: KB = ϕ implies KB C ϕ (University of Freiburg) Foundations of AI 29 / 42

30 Resolution: Idea We want a way to derive new formulae that does not depend on testing every interpretation. Idea: To prove that KB = ϕ, we can prove that KB { ϕ} is unsatisfiable (contradiction theorem). Therefore, in the following we attempt to show that a set of formulae is unsatisfiable. Condition: All formulae must be in CNF. But: In most cases, the formulae are close to CNF (and there exists a fast satisfiability-preserving transformation - Theoretical Computer Science course). Nevertheless: In the worst case, this derivation process requires an exponential amount of time (this is, however, probably unavoidable). (University of Freiburg) Foundations of AI 30 / 42

31 Resolution: Representation Assumption: All formulae in the KB are in CNF. Equivalently, we can assume that the KB is a set of clauses. E.g: Replace {(P Q) (R P ) S} by {{P, Q}, {R, P }, {S}} Due to commutativity, associativity, and idempotence of, clauses can also be understood as sets of literals. The empty set of literals is denoted by. Set of clauses: Set of literals: C, D Literal: l Negation of a literal: l An interpretation I satisfies C iff there exists l C such that I = l. I satisfies if for all C : I = C, i.e., I =, I = { }, for all I. (University of Freiburg) Foundations of AI 31 / 42

32 The Resolution Rule C 1 {l}, C 2 {l} C 1 C 2 C 1 C 2 are called resolvents of the parent clauses C 1 {l} and C 2 {l}. l and l are the resolution literals. Example: {a, b, c} resolves with {a, d, c} to {a, b, d}. Note: The resolvent is not equivalent to the parent clauses, but it follows from them! Notation: R( ) = {C C is a resolvent of two clauses from } (University of Freiburg) Foundations of AI 32 / 42

33 Derivations We say D can be derived from using resolution, i.e., D, if there exist C 1, C 2, C 3,..., C n = D such that C i R( {C 1,..., C i 1 }), for 1 i n. Lemma (soundness) If D, then = D. Proof idea: Since all D R( ) follow logically from, the lemma results through induction over the length of the derivation. (University of Freiburg) Foundations of AI 33 / 42

34 Completeness? Is resolution also complete? I.e., is valid? In general: no. E.g. consider: = ϕ implies ϕ {{a, b}, { b, c}} = {a, b, c} {a, b, c} But it can be shown that resolution is refutation-complete: is unsatisfiable implies Theorem: is unsatisfiable iff With the help of the contradiction theorem, we can show that KB = ϕ. Idea: KB { ϕ} is unsatisfiable iff KB = ϕ (University of Freiburg) Foundations of AI 34 / 42

35 Resolution: Overview Resolution is a refutation-complete proof process. There are others (Davis-Putnam Procedure, Tableaux Procedure,... ). In order to implement the process, a strategy must be developed to determine which resolution steps will be executed and when. In the worst case, a resolution proof can take exponential time. This, however, very probably holds for all other proof procedures. For CNF formulae in propositional logic, the Davis-Putnam Procedure (backtracking over all truth values) is probably (in practice) the fastest complete process that can also be taken as a type of resolution process. (University of Freiburg) Foundations of AI 35 / 42

36 Where is the Wumpus? The Situation (University of Freiburg) Foundations of AI 36 / 42

37 Where is the Wumpus? Knowledge of the Situation B = Breeze, S = Stench, B i,j = there is a breeze in (i, j) S 1,1 B 1,1 S 2,1 B 2,1 S 1,2 B 1,2 Knowledge about the wumpus and smell: R 1 : S 1,1 W 1,1 W 1,2 W 2,1 R 2 : S 2,1 W 1,1 W 2,1 W 2,2 W 3,1 R 3 : S 1,2 W 1,1 W 1,2 W 2,2 W 1,3 R 4 : S 1,2 W 1,3 W 1,2 W 2,2 W 1,1 To show: KB = W 1,3 (University of Freiburg) Foundations of AI 37 / 42

38 Clausal Representation of the Wumpus World Situational knowledge: S 1,1, S 2,1, S 1,2 Knowledge of rules: Knowledge about the wumpus and smell: R 1 : S 1,1 W 1,1, S 1,1 W 1,2, S 1,1 W 2,1 R 2 :..., S 2,1 W 2,2,... R 3 :... R 4 : S 1,2 W 1,3 W 1,2 W 2,2 W 1,1... Negated goal formula: W 1,3 (University of Freiburg) Foundations of AI 38 / 42

39 Resolution Proof for the Wumpus World Resolution: W 1,3, S 1,2 W 1,3 W 1,2 W 2,2 W 1,1 S 1,2 W 1,2 W 2,2 W 1,1 S 1,2, S 1,2 W 1,2 W 2,2 W 1,1 W 1,2 W 2,2 W 1,1 S 1,1, S 1,1 W 1,1 W 1,1 W 1,1, W 1,2 W 2,2 W 1,1 W 1,2 W 2,2... W 2,2, W 2,2 (University of Freiburg) Foundations of AI 39 / 42

40 From Knowledge to Action We can now infer new facts, but how do we translate knowledge into action? Negative selection: Excludes any provably dangerous actions. A 1,1 East A W 2,1 Forward Positive selection: Only suggests actions that are provably safe. Differences? A 1,1 East A W 2,1 Forward From the suggestions, we must still select an action. (University of Freiburg) Foundations of AI 40 / 42

41 Problems with Propositional Logic Although propositional logic suffices to represent the wumpus world, it is rather involved. Rules must be set up for each square. R 1 : S 1,1 W 1,1 W 1,2 W 2,1 R 2 : S 2,1 W 1,1 W 2,1 W 2,2 W 3,1 R 3 : S 1,2 W 1,1 W 1,2 W 2,2 W 1,3... We need a time index for each proposition to represent the validity of the proposition over time further expansion of the rules. More powerful logics exist, in which we can use object variables. First-Order Predicate Logic (University of Freiburg) Foundations of AI 41 / 42

42 Summary Rational agents require knowledge of their world in order to make rational decisions. With the help of a declarative (knowledge-representation) language, this knowledge is represented and stored in a knowledge base. We use propositional logic for this (for the time being). Formulae of propositional logic can be valid, satisfiable, or unsatisfiable. The concept of logical implication is important. Logical implication can be mechanized by using an inference calculus resolution. Propositional logic quickly becomes impractical when the world becomes too large (or infinite). (University of Freiburg) Foundations of AI 42 / 42

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